Segmentation and tracking of live cells in phase‐contrast images using directional gradient vector flow for snakes

Seroussi, I.; Veikherman, Danny; Ofer, Noa; Yehudai‐Resheff, Shlomit; Keren, Kinneret

doi:10.1111/j.1365-2818.2012.03624.x

Cited by 55 publications

(34 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Gradient based approaches are also of interest to many researchers [6,23]. In [6], behaviors of the fibroblast cells are studied and mitotic events are tracked which appeared as bright spots in their images.…”

Section: Prior Workmentioning

confidence: 99%

Image segmentation of mesenchymal stem cells in diverse culturing conditions

Afridi

Liu

Chan

et al. 2014

IEEE Winter Conference on Applications of Computer Vision

View full text Add to dashboard Cite

Researchers in the areas of regenerative medicine and tissue engineering have great interests in understanding the relationship of different sets of culturing conditions and applied mechanical stimuli to the behavior of mesenchymal stem cells (MSCs). However, it is challenging to design a tool to perform automatic cell image analysis due to the diverse morphologies of MSCs. Therefore, as a primary step towards developing the tool, we propose a novel approach for accurate cell image segmentation. We collected three MSC datasets cultured on different surfaces and exposed to diverse mechanical stimuli. By analyzing existing approaches on our data, we choose to substantially extend binarization-based extraction of alignment score (BEAS) approach by extracting novel discriminating features and developing an adaptive threshold estimation model. Experimental results on our data shows our approach is superior to seven conventional techniques. We also define three quantitative measures to analyze the characteristics of images in our datasets. To the best of our knowledge, this is the first study that applied automatic segmentation to live MSC cultured on different surfaces with applied stimuli.

show abstract

Section: Prior Workmentioning

confidence: 99%

Image segmentation of mesenchymal stem cells in diverse culturing conditions

Afridi

Liu

Chan

et al. 2014

IEEE Winter Conference on Applications of Computer Vision

View full text Add to dashboard Cite

show abstract

“…4, a and b). Phase images were segmented using the directional gradient vector flow algorithm (18), with manual adjustment near the lamellipod region.…”

Section: Image Processingmentioning

confidence: 99%

Disentangling Random Motion and Flow in a Complex Medium

Koslover¹,

Chan²,

Theriot³

2017

Biophysical Journal

View full text Add to dashboard Cite

We describe a technique for deconvolving the stochastic motion of particles from large-scale fluid flow in a dynamic environment such as that found in living cells. The method leverages the separation of timescales to subtract out the persistent component of motion from single-particle trajectories. The mean-squared displacement of the resulting trajectories is rescaled so as to enable robust extraction of the diffusion coefficient and subdiffusive scaling exponent of the stochastic motion. We demonstrate the applicability of the method for characterizing both diffusive and fractional Brownian motion overlaid by flow and analytically calculate the accuracy of the method in different parameter regimes. This technique is employed to analyze the motion of lysosomes in motile neutrophil-like cells, showing that the cytoplasm of these cells behaves as a viscous fluid at the timescales examined.

show abstract

“…However, this method is particularly sensitive to fluctuations often observed in biological data, such as cell-to-cell variation, low signal images or uneven illumination. Therefore, segmentation methods based on thresholding are often used as an initial step that is refined by other algorithms such as active contours [24], watersheds [25], morphological operations [26][27][28], or machine learning methods [29]. In addition, segmentation algorithms that use alternative detection strategies were developed, including multi-scale wavelets [30], cross-correlations [31] or likelihood criteria [32].…”

Section: Introductionmentioning

confidence: 99%